Thinking by classes in Data Science: the symbolic data analysis paradigm
Diday, Edwin (2016), Thinking by classes in Data Science: the symbolic data analysis paradigm, Wiley Interdisciplinary Reviews. Computational Statistics, 8, 5, p. 172–205. 10.1002/wics.1384
TypeArticle accepté pour publication ou publié
Journal nameWiley Interdisciplinary Reviews. Computational Statistics
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CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Abstract (FR)Penser en terme de classes en Science des données
Abstract (EN)Data Science, considered as a science by itself, is in general terms, the extraction of knowledge from data. Symbolic data analysis (SDA) gives a new way of thinking in Data Science by extending the standard input to a set of classes of individual entities. Hence, classes of a given population are considered to be units of a higher level population to be studied. Such classes often represent the real units of interest. In order to take variability between the members of each class into account, classes are described by intervals, distributions, set of categories or numbers sometimes weighted and the like. In that way, we obtain new kinds of data, called ‘symbolic’ as they cannot be reduced to numbers without losing much information. The first step in SDA is to build the symbolic data table where the rows are classes and the variables can take symbolic values. The second step is to study and extract new knowledge from these new kinds of data by at least an extension of Computer Statistics and Data Mining to symbolic data. SDA is a new paradigm which opens up a vast domain of research and applications by giving complementary results to classical methods applied to standard data. SDA also gives answers to big data and complex data challenges as big data can be reduced and summarized by classes and as complex data with multiple unstructured data tables and unpaired variables can be transformed into a structured data table with paired symbolic‐valued variables.
Subjects / Keywordsdata science; data mining; classification; learning; symbolic data analysis; functional analysis; Bayesian; multilevel analysis; complex data; big data; granular computing; compositional data; Science des données; Apprentissage Automatique à base de corpus
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